Why the study?
Sepsis has a highly variable clinical course requiring continuous monitoring to guide intravenous fluids and vasopressors, but optimal treatment remains debated.
Combining deep reinforcement learning with mechanistic cardiovascular models provides a novel, explainable framework for personalized sepsis treatment and uncertainty-aware decision support.
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Hypothesis-generating for explainable AI sepsis tools; leaves open outcome benefits in prospective trials.
Sepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty-aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies, that are consistent with clinical knowledge. Further our method consistently identifies high-risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research.
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Nanayakkara et al. (2022) studied this question.
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